C-TREND: Temporal Cluster Graphs
Identifying and Visualizing Trends in
Multiattribute Transactional Data
Organizations and firms are capturing increasingly more data about
their customers, suppliers, competitors, and business environment.
Most of this data is multiattribute (multidimensional) and temporal
in nature. Data mining and business intelligence techniques are
often used to discover patterns in such data; however, mining
temporal relationships typically is a complex task. This paper
propose a new data analysis and visualization technique for
representing trends in multiattribute temporal data using a
clustering based approach. This paper introduce Cluster-based
Temporal Representation of EvenT Data (C-TREND), a system
that implements the temporal cluster graph construct, which maps
multiattribute temporal data to a two-dimensional directed graph
that identifies trends in dominant data types over time.
This paper present temporal clustering-based
technique, discuss its algorithmic implementation and
performance, demonstrate applications of the
technique by analyzing data on wireless networking
technologies and baseball batting statistics, and
introduce a set of metrics for further analysis of
Existing algorithms uses matrices to produce
Distance between the matrices is used for
Existing Schemes Consumes more time.
In our project we use DENDROGRAM Data
structure for storing and Extracting cluster
solutions generated by hierarchical clustering
Calculations are made using Tree structure
Efficiency is considerably increased.
N is user defined.
Transformation of data’s from excel sheet
Creation of dataset
Partition and clustering
Display the time of sorting.
Extracting values according to N
Processor : Pentium IV
Hard Disk : 80 GB.
RAM : 512 MB.
Operating system : Windows XP
Technology : Java 1.6
Web-Server :Tomcat 5.5
Data base :SQL Server 2000